Image analysis of the golgi complex

ABSTRACT

Methods, code and apparatus analyze cell images to automatically identify and characterize the Golgi complex in individual cells. This is accomplished by first locating the cells in the image and defining boundaries of those cells that subsume some or all of the Golgi complex of those cells. The Golgi complex in the images typically have intensity values corresponding to the concentration of a Golgi component in the cell (e.g. a polysaccharide associated with the Golgi complex). The method/system then analyzes the Golgi components of the image (typically on a pixel-by-pixel basis) to mathematically characterize the Golgi complex of individual cells. This mathematical characterization represents phenotypic information about the cells&#39; Golgi complex and can be used to classify cells. From this information, mechanism of action and other important biological information can be deduced.

CROSS-REFEERNCE TO RELATED PATENT APPLICATIONS

This application is a CIP of 09/729,754 now U.S. Pat. No. 6,876,760issued Apr. 5, 2005 which in turn is a CIP of 09/310,879 filed May 14,1999; 09/311,996 filed May 14, 1999; and of 09/311,890 filed May 14,1999 now Pat. No. 6,743,576, issued Jun. 1, 2004.

BACKGROUND OF THE INVENTION

The present invention relates to methods and apparatus forcharacterizing a cell's condition based upon the state of its Golgiorganelle. More specifically, the invention relates to image analysismethods and apparatus that rapidly characterize a cell based uponphenotypic characteristics of its Golgi complex.

The Golgi complex is a central and complex organelle within eukaryoticcells. It is involved in the sorting, modifying, and transporting ofcellular molecules. It plays important roles in intracellular processesincluding endocytosis, exocytosis, and transport between cellularorganelles. A detailed discussion of the role of the Golgi complex incellular processes can be found in various treatises on cell biology.One example is Alberts et al. “Molecular Biology of the Cell” GarlandPublishing, Inc., which is incorporated herein by reference for allpurposes.

The Golgi complex can be an important indicator of the cellular effectscaused by certain external agents. As a phenotypic characteristic, theGolgi complex has considerable value in drug discovery and fundamentalbiological research. For example, certain drugs and geneticmodifications subtly affect transport pathways within a cell in waysthat are manifest by the Golgi condition. Unfortunately, the value ofthe Golgi complex as an indicator has not been fully realized. This isbecause no simple consistent technique has been developed forcharacterizing the condition of Golgi complex in a high throughputmanner.

SUMMARY OF THE INVENTION

This invention addresses the above need by providing methods, code andapparatus that analyze cell images to automatically identify andcharacterize Golgi in individual cells. The invention accomplishes thisby first locating the cells in the image and defining subregions ofthose cells that subsume some or all of the Golgi of those cells. TheGolgi complex in the images typically have intensity valuescorresponding to the concentration of a Golgi component in the cell(e.g. a polysaccharide associated with the Golgi complex). Themethod/system then analyzes the Golgi components of the image tomathematically characterize the Golgi complex of individual cells. Thismathematical characterization represents phenotypic information aboutthe cells' Golgi complex and can be used to inferbiological/physiological state of cells. From this information,mechanism of action and other important biological information can bededuced.

One aspect of this invention pertains to a method of analyzing an imageof one or more cells. This method may be characterized by the followingsequence of operations (typically implemented on a computing device):(a) identifying a region of the image subsuming some or all of the Golgicomplex of a single cell; (b) within this region, automaticallyidentifying the location of the Golgi complex; and (c) automaticallymathematically characterizing the Golgi complex within the single cell.Preferably, the mathematical characterization is based upon (i) theGolgi complex location within the region and/or (ii) the concentrationof a Golgi component within the region,

In addition to a basic characterization of the Golgi complex, whichtypically involves some basic morphological or statisticalcharacterization, the process may automatically classify the Golgicomplex in a category that at least partially distinguishes betweennormal Golgi and Golgi that is either diffuse or disperse or bothdiffuse and disperse. Such classification may be accomplished using abiological model, in the form of a neural network or regression model(e.g. a CART), for example.

Regarding the operation of identifying the region of the image subsumingsome or all of the Golgi complex, the process may “segment” the imageinto multiple regions, each subsuming at least part of an individualcell. In a one example, segmentation involves identifying locations ofnuclei in the cells, and then dilating the locations of the nuclei tosubsume the locations of some or all of the Golgi complex.

Many different mathematical characterizations of the Golgi complex maybe made. Preferably, all of them have biological relevance. Examples oftypes of mathematical characterizations include (i) an indicator of thepeakedness of a histogram of at least one component of the Golgicomplex, (ii) the texture of the Golgi complex and (iii) and the amountof Golgi complex in the region. As specific examples, the mathematicalcharacterization of the Golgi complex include the kurtosis of intensityvalues obtained from the image, eigenvalues of a singular valuedecomposition of intensity values obtained from the image, and at leastone of a mean and a standard deviation of intensity values obtained fromthe image.

Another aspect of this invention pertains to apparatus for automaticallyanalyzing an image of one or more cells. Such apparatus may becharacterized by the following features: (a) an interface configured toreceive the image of one or more cells; (b) a memory for storing, atleast temporarily, some or all of the image; and (c) one or moreprocessors in communication with the memory and designed or configuredto segment the image into discrete regions, each subsuming some or allof the Golgi complex in single cell. The processors may additionallycharacterize the Golgi complex of single cells by operating on thediscrete regions. Still further, the processors may be designed orconfigured to classify the Golgi complex based upon suchcharacterization of the Golgi complex. In one example, theclassification distinguishes between normal Golgi and Golgi that isdiffuse or disperse. The classification may make use of a biologicalmodel, in the form of a classification and regression tree, for example.

Still another aspect of the invention pertains to methods of producing amodel for classifying cells based upon the condition of Golgi within thecells. Such method may be characterized by the following sequence: (a)receiving images of a plurality of cells of a training set; (b)analyzing the images to mathematically characterize the Golgi within themultiple cells from the training set; and (c) applying a modelingtechnique to the mathematical characterizations obtained in (b) tothereby produce the model. Typically, the training set will containindividual cells having Golgi in various states. The various states ofGolgi in the cells of the training set may be produced, at least inpart, by treatment with multiple exogenous agents such as drugs or drugcandidates.

The process of analyzing the images mathematically may involve some ofthe operations outlined above such as segmentation, characterization,and classification. In one preferred approach, the modeling techniquecomprises generating a classification and regression tree.

Yet another aspect of the invention pertains to computer programproducts including machine-readable media on which are stored programinstructions for implementing a portion of or an entire method asdescribed above. Any of the methods of this invention may berepresented, in whole or in part, as program instructions that can beprovided on such computer readable media. In addition, the inventionpertains to various combinations of data generated and/or used asdescribed herein.

These and other features and advantages of the present invention will bedescribed in more detail below with reference to the associated figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A–1D depict, in cartoon fashion, cells having Golgi in normal,dispersion, diffusion, and diffusion/dispersion states, respectively.

FIG. 2 is a block diagram of an architecture and data flow for usingmultiple image analysis algorithms, each handling a different image of amicroscopic field.

FIG. 3 is a process flow diagram depicting one image analysis processfor characterizing Golgi in accordance with this invention.

FIG. 4A generally illustrates how an image of multiple cells may besegmented to provide separate representations of individual cells andthereby allow a cell-by-cell analysis.

FIG. 4B generally depicts a representation of a cell as a binary objectas identified using an edge finding routine.

FIGS. 5A and 5B depict a technique for “dilating” an image of a nucleusto create a perinuclear ring region where normal Golgi tend to localize.

FIG. 6 is an intensity histogram showing peaked and flat curvescorresponding to normal and diffuse Golgi, respectively.

FIG. 7 is a cartoon depiction of cellular Golgi showing how normal anddispersed Golgi segregate inside and outside the perinuclear region,respectively.

FIG. 8 is a sample classification and regression tree of the type thatmay be employed to classify Golgi in accordance with this invention.

FIG. 9 is a block diagram of a computer system that may be used toimplement various aspects of this invention such as the various imageanalysis algorithms of this invention.

DETAILED DESCRIPTION OF INVENTION

A premise of this invention is that the shape and arrangement of Golgiin a cell provides valuable information about the cell's condition. Ifsuch condition results from treatment with a drug or other exogenousagent, then the Golgi complex may shed light on a mechanism of action.

In most normal cells, the Golgi complex is concentrated in a perinuclearregion. In other words, normal Golgi is located in a cell's cytoplasmproximate to the nucleus. The chemical composition of the Golgi complex(and associated organelles and vacuoles) also affects the state of theGolgi within a cell's cytoplasm. Thus, when the particular drug or otherexogenous agent affects a cell's cytoskeletal components and/or Golgichemistry, this mechanism of action can be identified or suggested byexamining the cell's Golgi complex.

In accordance with this invention, image analysis of the Golgi complexmay be used to characterize the effects of any number of cellularstimuli. Stimuli of interest may include exposure to materials,radiation (including all manner of electromagnetic and particleradiation), forces (including mechanical, electrical, magnetic, andnuclear), fields, and the like. Examples of materials that may be usedas stimuli include organic and inorganic chemical compounds, biologicalmaterials such as nucleic acids, carbohydrates, proteins and peptides,lipids, various infectious agents, mixtures of the foregoing, and thelike. Other specific examples of agents include exposure to differenttemperatures, non-ambient pressure, acoustic energy, electromagneticradiation of all frequencies, the lack of a particular material (e.g.,the lack of oxygen as in ischemia), etc.

Turning now to FIGS. 1A, 1B, 1C, and 1D, the Golgi complex may exist infour separate and identifiable end states. Other detectable end statesmay also exist. For purposes of illustration, however, this documentwill focus on the four states depicted in FIGS. 1A–1D. FIG. 1A depicts acell in an interphase stage of the cell cycle. Many cells have Golgigenerally located as depicted, so long as they are not experiencing amajor perturbation. As shown, a cell 101 includes a nucleus 103 and theGolgi apparatus 105. In this “normal” cell, the Golgi complex lies closeto the nucleus 103 primarily on one side of the nucleus. A region of thecell where the Golgi complex resides is also proximate to themicrotubule organizing center (MTOC), a structure within the cells fromwhich the microtubules 107 radiate. Normal localization and morphologyof the Golgi complex 105 is mediated by, among other factors,microtubules 107 and microtubule motors, proteins that drive the motionof various cellular components along microtubules.

Some changes in physiological conditions of the cell might causefragmentation of the Golgi apparatus. Golgi in this condition is said tobe “disperse.” FIG. 1B depicts, in cartoon fashion, a cell 109 in whichGolgi fragments 111 are dispersed throughout the cytoplasm. This stateof the Golgi complex may arise, for example, when the microtubulecomponent of the cytoskeleton has been disrupted. Some drugs, forexample, cause depolymerization of tubulin. When this occurs, the Golgicomplex no longer remains confined to tight bands in the MTOC. Rather,it disperses throughout the cytoplasm as depicted in FIG. 1B. Otherexogenous agents may disrupt microtubule motors. This too can causedispersion of the Golgi complex. In general, exogenous agents thatdisrupt the microtubule cytoskeleton, either directly or indirectly,lead to dispersion of the Golgi complex.

FIG. 1C depicts a third end state of the Golgi organelle. As shown therein cartoon fashion, a cell 113 has had its Golgi 115 spread throughoutthe cytoplasm without fragmentation. Here again the Golgi complex nolonger remains confined to the perinuclear area. However, rather thanforming discrete fragments it diffuses more or less evenly throughoutthe cells cytoplasm. Golgi in this condition is said to be in a“diffuse” state.

Golgi diffusion typically results when the dynamic barrier between theGolgi complex and endoplasmic reticulum compartments is disrupted tosome degree. Actually, at this point, there may be no distinct Golgi.Rather the endoplasmic reticulum and Golgi have become one entity. Thiscondition may result from a direct or indirect effect on the pathwaysbetween the endoplasmic reticulum and the Golgi complex. Any of a numberof different exogenous and endogenous influences may cause this. In theend, the chemistry of the Golgi membrane changes significantly from itsnormal state.

The various states depicted in FIGS. 1A–1C represent “end states” inwhich the Golgi complex has reached an extreme. Obviously, dependingupon the level of an effect causing cell disruption, the Golgi complexmay exist in any one of these states only to a limited degree. Thus, forexample, a cell may exist may exist in a state between diffusion anddispersion as depicted in FIG. 1D. As shown there, a cell 117 includesboth diffuse Golgi components 115 and disperse Golgi components 111. Inpreferred embodiments, this invention can determine the degree to whicha particular cell resides in any one of the 4 states depicted in FIGS.1A–1D.

Note also that the some of the influences may affect absolute amounts ofGolgi components interacting with a particular Golgi marker. Someembodiments of this invention characterize the cell based upon theconcentration of a marked Golgi component, which is affected by thedegree to which an amount of a chemical constituent (or constituents) ofthe Golgi complex has changed from its normal state.

Note that the normal state depicted in FIG. 1A is normal only forinterphase cells, not for mitotic cells. Golgi naturally becomesdispersed and/or diffuse during mitosis. When a large number of dividingcells are present, these effects can obscure detection of interestingchanges to the Golgi complex that one might wish to detect. Therefore,the present invention may include operations that distinguish betweenmitotic and interphase cells. In the subsequent discussion, much of thecharacterization of the Golgi complex assumes that an algorithm hasalready limited consideration to interphase cells.

Preparation of the Image

Generally the images used as the starting point for the methods of thisinvention are obtained from cells that have been specially treatedand/or imaged under conditions that contrast the cell's Golgi from othercellular components and the background of the image. In the preferredembodiment, the cells are fixed and then treated with a labeling agent(i.e., a marker) that binds to one or more Golgi components and shows upin an image. Preferably, the chosen agent specifically binds to cellcomponents that are enriched in the Golgi complex. The agent shouldprovide a strong signal to show where it is concentrated in a givencell. To this end, the agent may be luminescent, radioactive,fluorescent, etc. Various stains and fluorescent compounds may servethis purpose.

The Golgi complex has numerous components that can be marked forgenerating images for use with this invention. For example, the Golgicontains specific proteins, lipids, and polysaccharides that can bemarked for analysis. Some of these components occur in much higherconcentration within the Golgi complex than within other cellularcomponents. In one preferred embodiment, the Golgi complex is identifiedusing fluorescently labeled antibodies that bind specifically to a Golgicomponent. In a particularly preferred embodiment, the Golgi complex isidentified by treatment with labeled Lens culinaris lectin (LC lectin).Lectins generally bind very specifically to certain polysaccharides. LClectin binds to polysaccharide chains on proteins (protoglycans) havinga N-acetyl-d-glucosearnine residue at the end of the polysaccharidechain. Alternatively one could use antibodies to proteins enriched inthe Golgi complex, such as gp130, [beta]COP.

Various techniques for preparing and imaging appropriately treated cellsare described in U.S. patent application Ser. Nos. 09/310,879,09/311,996, and 09/311,890, previously incorporated by reference. In thecase of cells marked with a fluorescent material, a collection of suchcells is illuminated with light at an excitation frequency. A detectoris tuned to collect light at an emission frequency. The collected lightis used to generate the image and highlights regions of high Golgicomponent concentration.

Sometime corrections must be made to the measured intensity. This isbecause the absolute magnitude of intensity can vary from image to imagedue to non-linearities in the image acquisition procedure and/orapparatus. Specific optical aberrations can be introduced by variousimage collection components such as lenses, filters, beam splitters,polarizers, etc. Other non-linearities may be introduced by anexcitation light source, a broad band light source for opticalmicroscopy, a detector's detection characteristics, etc. For example,some optical elements do not provide a “flat field.” As a result, pixelsnear the center of the image have their intensities exaggerated incomparison to pixels at the edges of the image. A correction algorithmmay be applied to compensate for this effect. Such algorithms can beeasily developed for particular optical systems and parameter setsemployed using those imaging systems. One simply needs to know theresponse of the systems under a given set of acquisition parameters.

General Algorithms for Image Analysis

Various algorithms can be used to analyze cell images that highlightGolgi. Certain specific examples will be described herein. In general,much of the relevant information associated with Golgi can be obtainedby specifically analyzing interphase cells and ignoring, or treatingseparately, mitotic cells. Preferably, algorithms of this inventionidentify the normal, diffuse, and disperse states of Golgi withininterphase cells. To this end, the algorithm may obtain certainmorphological and/or statistical information about the Golgi complexfrom the image and use that information to classify the Golgi.

Most images of relevance will depict the Golgi complex as variations inintensity over position in the image, with higher intensity regions inthe image corresponding to regions of the cell where the Golgi componentexists in relatively high concentrations. Examples of morphological andstatistical image characteristics that can be used to effectivelycharacterize the Golgi include the “peakedness” of a histogram of pixelintensities, the texture of the Golgi regions of the image, the overallor total intensity obtained in the image, and the moments of the Golgicomplex about a nucleus identified in a cell image.

Often it will be desirable to use an image analysis algorithm for Golgiin conjunction with image analysis algorithms for other components of acell. In fact, in a preferred algorithm described herein, it isnecessary to first identify the nucleus of a cell prior to identifyingthe Golgi complex.

FIG. 2 depicts, from a global perspective, an algorithm (or group ofalgorithms) that characterizes a cell in terms of its nuclei, Golgi, andtubulin. As shown in FIG. 2, a global image analysis 200 begins with oneor more images 201 as input. In this example, image 201 possesses threeseparate “channels.” Each of these channels represents a different labelthat can be separately captured by the imaging apparatus and representeddistinctly in an image of the cell or a field of cells. In a typicalexample, the separate labels are separate fluorophores having distinctemission frequencies. In a very specific example, cells are treated witha marker for DNA emitting in blue, a marker for the Golgi complexemitting in green, and a marker for tubulin emitting in red. As depictedin FIG. 2, the image analysis algorithm 200 includes separate processbranches: a nuclei analysis branch 203, a Golgi analysis branch 205, atubulin analysis branch 207.

Each of these branches has its own separate algorithm for analyzing theimage. As shown, the nuclei are analyzed with an algorithm 209, theGolgi complex are analyzed with an algorithm 211, and the tubulin isanalyzed with a algorithm 213. Each of these algorithms act oninformation taken from the channel specific to the cell component ofinterest. The output of each of these algorithms is the combination ofextracted features and, possibly (not necessarily), a separateclassification or characterization of the associated cell componentaccording to its state in a particular cell. As depicted, the output ofalgorithm 209 is the combination of identified individual nuclei (thisis the part of the output that is later used by the Golgi algorithm),their morphological and intensity features, and a classification of thecell based upon its DNA or nucleus features. In a preferred embodiment,block 215 classifies the cell based upon its stage in the cell cycle.The output of algorithm 211 are Golgi features which are input to 217for classification. Preferably, this classification is based upon (1)the type of Golgi arrangement (e.g. the degree of diffusion, dispersion,and normalcy) and/or (2) the concentration of one or more Golgicomponents (depicted as intensity of signal emitted by one or more Golgimarkers). Finally, the output of tubulin algorithm 213 is the tubulinfeatures which are input to 219 for classification. In one embodiment,this classification characterizes the overall shape of a cell.

FIG. 3 depicts a process flow that may be employed to characterize Golgiappearing in images of cells. The process depicted in FIG. 3 (identifiedby the reference 300) may serve as a combination of algorithms 209 and211 depicted in FIG. 2, for example.

Initially, the individual cells, or at least the regions of those cellsharboring the Golgi complex, are identified. Generally, this process isreferred to as “segmentation.” This can be accomplished in numerousways. In the embodiment depicted in FIG. 3, it is accomplished inoperations 301 and 303. In this example, the image analysis toolinitially identifies the nucleus of each cell captured in the imageunder consideration. Since images from different channels can be wellregistrated, nucleus can be first identified in the DNA channel and thenoverlaid to the image under consideration. Identifying nucleus in theDNA channels may be accomplished using any of a number of nucleusidentification/segmentation algorithms. As shown in FIG. 2, the outputof nucleus algorithm 209 is provided to Golgi algorithm 211.

After the nuclei have been identified at 301, the Golgi image analysisroutine next defines a “ring region” around each nucleus. See 303.Generally, this step serves to define the perinuclear region. It will bedescribed in detail with reference to FIGS. 4A and 4B. For now, itsufficient to understand that the purpose of operation 303 is to definea region that will encompass or subsume some or all of the Golgi complexin a normal interphase cell.

Within the regions defined in operation 303, the features of the Golgicomplex may be identified using intensity versus position data. Forexample, each pixel in the ring region is associated with a particularintensity, at the channel in which the Golgi complex is imaged. Fromsuch intensity data, various morphological and/or statistical featuresmay be extracted in order to characterize the Golgi complex. See 305.These features will be described in more detail below. In oneparticularly interesting embodiment, the features of interest includekurtosis, standard deviation, mean, and singular value decomposition,all obtained from the intensities of the pixels in the ring region.

The statistical and morphological features can provide very usefulinformation for characterizing Golgi complex. In themselves, however,these parameters have no biological meaning. In a preferred embodiment,algorithm 300 converts information contained in these parameters tobiologically relevant classifications. This operation is depicted inblock 307. In a preferred embodiment, operation 307 classifies all cellsinto one of four primary Golgi states: normal, diffuse, disperse, anddiffuse/disperse.

Further biologically relevant information can be obtained by consideringan entire population of cells, and their associated Golgi states. Thus,algorithm 300 concludes with an operation 309, in which a populationanalysis is conducted. In one example, the relative percentages of cellsin any of the stages of cell cycle having Golgi in each of the fourabove-mentioned end states is computed from data on Golgi status andcell cycle stage of each individual cell. Note that statistics of theGolgi status may be computed independently for interphase and mitoticcells.

Segmentation

One approach to segmentation is depicted in FIG. 4A. As shown there, animage 401 includes a plurality of cell nuclei images 403 identified, forexample, with a DNA-binding marker. Segmentation effectively convertsimage 401 into discrete images/representations for the DNA of each cellas shown at 405. In a preferred embodiment, this collection ofrepresentation 405 is provided as a mask providing intensity as afunction of position for each cell nucleus in image 401.

Individual cell representations 405 may be extracted from image 401 byvarious image analysis procedures. Preferred approaches include edgefinding routines and threshold routines. Some edge finding algorithmsidentify pixels at locations where intensity is varying rapidly. Forthreshold routines, pixels contained within the edges will have a higherintensity than pixels outside the edges. Threshold algorithms convertall pixels below a particular intensity value to zero intensity in animage subregion (or the entire image, depending upon the specificalgorithm). The threshold value is chosen to discriminate between nucleiimages and background. All pixels with intensity values above thresholdin a given neighborhood are deemed to belong to a particular cellnucleus.

The concepts underlying thresholding are well known. A threshold valueis chosen to extract those features of the image having intensity valuesdeemed to correspond to actual cells (nuclei). Typically an image willcontain various peaks, each having collections of pixels with intensityvalues above the threshold. Each of the peaks is deemed to be a separate“cell” or “nucleus” for extraction during segmentation.

An appropriate threshold may be calculated by various techniques. In aspecific embodiment, the threshold value is chosen as the mode (highestvalue) of a contrast histogram. In this technique, a contrast iscomputed for every pixel in the image. The contrast may be computed asthe intensity difference between a pixel and its neighbors. Next, foreach intensity value (0–255 in an eight byte image), the averagecontrast over all pixels in the image is computed. The contrasthistogram provides average contrast as a function of intensity. Thethreshold is chosen as the intensity value having the largest contrast.See “The Image Processing Handbook,” Third Edition, John C. Russ 1999CRC Press LLC IEEE Press, and “A Survey of Thresholding Techniques,” P.K. Sahoo, S. Soltani and A. K. C. Wong, Computer Vision, Graphics, andImage Processing 41, 233–260 (1988), both of which are incorporatedherein by reference for all purposes.

In another specific embodiment, edge detection may involve convolvingimages with the Laplacian of a Guassian filter. The operation performedon the image is given byf(x,y)=∫∫I(x−u,y−v)g(u,v)dudv

Here I is the intensity of a pixel, x and y are the coordinates in theoriginal image, and g is the Laplacian of a Guassian filter

$\frac{x^{2} + y^{2} - {2\sigma^{2}}}{2\;\pi\;\sigma^{8}}{{\mathbb{e}}^{\frac{x^{2} + y^{2}}{2\;\sigma^{2}}}.}$The zero-crossings of the resulting image f(x,y) are detected as edgepoints. The edge points are linked to form closed contours, therebysegmenting the relevant image objects. See The Image ProcessingHandbook, referenced above. FIG. 4B depicts the transformation in a verycourse fashion. An original image 422 is convolved with the Laplacian ofa Guassian filter to give a new image 424 which contains positive andnegative values at the component pixels. Note that this figure shows themask of an object, not detected edges/contours. Wherever the signchanges in moving from one pixel to the next a contour results.

Generation of Ring Region

As indicated in the discussion of 3, it is often desirable to generate aring about the cell nucleus (see block 303) in order to define a regionwhere normal Golgi are likely to reside. This operation is depicted incartoon fashion in FIG. 5A. As shown there, an image of a cell's nucleus503 is bounded by a ring region 505. Within ring region 505, a normalGolgi organelle 507 appears in the image. As explained above, normalGolgi typically reside in the perinuclear region, which is coextensivewith ring region 505.

Note that the size of the ring region can be chosen to allow some or allof the normal Golgi to be subsumed. In a preferred embodiment, the sizeof ring region 505 is chosen to subsume all of the normal Golgi in amajority of interphase cells 507. Diffuse and dispersed Golgi typicallyare not confined to a perinuclear region. Therefore, ring region 505 maynot, depending upon the ring width setting, subsume all the Golgi inthese states. The algorithm can use this fact to distinguish, or helpdistinguish, normal Golgi from diffuse and dispersed Golgi. Assumingthat the total “quantity of Golgi” is approximately consistent acrossnormal, diffuse, and disperse states, then the “amount of Golgi” locatedwithin ring region 505 will be greatest in the case of the normal Golgistate. Because some significant fraction of the Golgi lies outside ofring region 505 in the case of diffuse and/or disperse Golgi states, thetotal amount of Golgi detected within region 505 will be less in thesestates. In preferred embodiments, this is not the primary method used todeduce the Golgi state. Other features of pixel distributions (describedbelow) are used more typically.

The ring shaped region about the nucleus may be defined using varioustechniques. One suitable technique is depicted in FIG. 5B. As shownthere, an image segment 511 comprises a two-dimensional collection oflocations or pixels. Each of these is represented by a small rectangularbox. The pixels where the nucleus resides are shown with the referenceletter “N.” To define the ring region, the algorithm locates the nearestneighbor pixels about the edge of the nucleus. In one embodiment, thisis accomplished by identifying the four nearest neighbor pixels for eachpixel identified as belonging to the nucleus. Those neighboring pixelswhich themselves form part of the nucleus are not considered. In FIG.5B, these nearest neighbor pixels are identified by the reference letter“X.” To provide the appropriate width for the ring region, this processmay be repeated any number of times. In the example shown in FIG. 5B, itis repeated once-to provide a total of two iterations. In the seconditeration, each of the pixels considered in the first iteration is usedto identify four nearest neighbors. In FIG. 5B, the second iterationnearest neighbors are identified by the reference letter “O.”

The exact number of iterations used to define the ring region can varydepending upon the number of parameters. First, one must decide whetherthe ring region should subsume only Golgi residing in the closeperinuclear region or should subsume Golgi residing within a widerportion of the cell, or even the entire cell itself. Second, the desiredwidth of the ring region may vary depending upon the magnification ofthe image, the resolution of the image, and other related properties ofthe image.

Note that the mechanism depicted in FIG. 5B represents but a singleapproach to defining the ring region. Some other suitable techniqueswill be readily apparent to those of skill in the art. Further, othertechniques may be employed to define the region within which the Golgimay be subsumed. For example, some techniques will not consider the DNAor nucleus of a cell. Rather, they will simply focus on the image of thepertinent Golgi component and then consider appropriate morphologicaland/or statistical parameters to distinguish the Golgi of one cell fromthe Golgi of another cell. Still further, other techniques will considerother non-Golgi features of an image to distinguish one cell fromanother. For example, some algorithms may distinguish cells based uponimages of the plasma membrane, cytoskeleton, etc. U.S. patentapplication Ser. No. 09/792,013 previously incorporated by reference,describes a technique that uses tubulin (or other cytoskeletalcomponent) and DNA markers together to identify discrete cells.Regardless of which technique is employed, in the end, regions subsumingsome or all of the Golgi within discrete cells must be separatelyidentified. Then, the image of the appropriate Golgi marker can beanalyzed to characterize the Golgi in accordance with this invention.Note that while separate analysis of Golgi regions in each individualcell is often preferable, this is not a necessary requirement of thealgorithm. Upon creation of “ring” areas, all pixels in all of the ringscan be analyzed simultaneously.

Relevant Golgi Features

In order to effectively characterize the Golgi complex of individualcells, the present invention may make use of various parameters derivedfrom Golgi complex images. These parameters may represent the shape,size, concentration, texture, and amount of Golgi components in anindividual cell. In one preferred algorithm of the present invention,one set of parameters is used to characterize the “type” of Golgi andanother parameter or set of parameters is used to characterize theamount of Golgi complex in an individual cell. The types of Golgi mayinclude normal, diffuse, disperse, and disperse/diffuse as discussedabove. Of course, other suitable processes may consider different and/oradditional types of Golgi. The amount of Golgi may be determined fromthe local concentrations of a marked Golgi component summed over theentire region of interest (e.g. a ring region).

Note that while the discussion below is organized sequentially, thedescribed features of different nature may be used simultaneously toclassify Golgi states. The parameters chosen for use with this inventionshould allow discrimination between biologically relevantclassifications. Further, they may provide a continuous measure of thedegree to which an individual cell exhibits features of any of theclassifications. As mentioned, particularly interesting parameters willrelate to one or more of the following general features of the Golgicomplex image: the shape (such as peakedness) of an intensity (markerconcentration) histogram, the texture of the Golgi areas, the overallintensity (some of local marker concentrations) of the Golgi image, andthe moment (including moments of different orders) of the Golgi complexabout the nucleus. Another set of features for the Golgi complex arestatistics computed from a histogram of the intensity of pixelintensities by angle from the center of the nuclei. The x-axis of thishistogram is the angle of the vector connecting the center of the nucleito the ring pixel. The y-axis is the sum of the pixel intensities at agiven angle. When the histogram is unimodal the Golgi complex is likelyclassified as normal, if the histogram has many modes the Golgi complexis likely classified as dispersed and if the histogram is uniform theGolgi complex is likely classified as diffuse.

Regarding peakedness, FIG. 6 depicts sample histograms for normal anddiffuse Golgi. As shown in FIG. 6, a histogram plots the pixel count(number of pixels in an image meeting a criteria) as a function of pixelintensity. Remember that the intensity is a measure of the localconcentration of a marked component in the Golgi complex. A Golgicomplex with high local concentrations of the marked Golgi componentwill have a narrow intensity distribution as shown by curve 601. Adiffuse Golgi complex will have a wider distribution as illustrated bycurve 603.

Generally, normal Golgi, and to some degree dispersed Golgi, possesslocal regions of high concentration of marked Golgi component.Therefore, these types of Golgi will have histograms with relativelynarrow distributions. In contrast, diffuse Golgi complexes have theirmarked components relatively evenly distributed throughout most or allof the ring area. Therefore, the histogram of such diffuse Golgi willhave a relatively wide distribution. In FIG. 6, curve 601 mightrepresent the histogram of normal or dispersed Golgi, while curve 603might represent a histogram of diffuse Golgi.

Another parameter of interest in characterizing Golgi is the texture.The texture may be characterized by a number of parameters such as thesingular value decomposition of a matrix of intensity valuesrepresenting the marked Golgi component. Generally the texture of anobject relates to the size and shape of the granules or other componentsof an image. Various texture related features can help to discriminatebetween normal and dispersed Golgi and/or between dispersed anddiffuse/dispersed states.

The overall intensity associated with an image of the Golgi complex canrepresent the total amount of particular Golgi component(s) within agiven cell or region of a cell. FIG. 7 illustrates how this concept canbe used to distinguish between normal Golgi and dispersed Golgi using analgorithm that employs a ring region as described above. As shown inFIG. 7, a cell 701 possesses normal Golgi while a cell 703 possessesdispersed Golgi. Each cell includes a nucleus 705 and a plasma membrane707. In the case of cell 701, the Golgi complex (represented byreference number 709) resides entirely within a ring region 711. In thiscell, all of its Golgi resides within ring region 711. Therefore, thetotal intensity of a Golgi marker found within ring region 711 willrepresent all of the Golgi in cell 701. In contrast, cell 703 has acomparable ring region 711′ which subsumes only a fraction of the Golgiin that cell (709′). The remainder of the dispersed Golgi lies outsidethe ring region 711′. As a consequence, it may be expected that thetotal intensity of Golgi calculated for cell 703 (using ring region711′) will be less than the total intensity calculated for cell 701.

The biologically relevant features of the Golgi complex may be obtainedfrom various mathematical parameters. Among the parameters of interestare the following: the total area of the ring region in which the Golgimarker is considered, the mean intensity of all pixels in the ringregion, the standard deviation of the pixel intensities in ring region,the kurtosis of the pixel intensities in the ring region, and thesecond, third, and fourth largest eigenvalues obtained by a singularvalue decomposition of a matrix of pixels in the ring. Also, sometimes,the Golgi “type” (normal, diffuse, dispersed, or diffuse and dispersed)is treated as a parameter. This is technically a characterizationderived from parameters such as the SVD eigenvalues, the kurtosis, etc.In a particularly preferred embodiment, the following four relevantparameters are used to characterize the Golgi complex: mean, standarddeviation, kurtosis, and singular value decomposition.

Singular value decomposition and kurtosis can be derived from digitizedimages using conventional approaches. Kurtosis is given by m₄/(std)⁴,where std is the standard deviation of intensity of the pixels in thering region in the image and m₄ is given by the following expression:

$m_{4} = {\frac{1}{N}{\overset{N}{\sum\limits_{i = 1}}\left( {x_{i} - \overset{\sim}{x}} \right)^{4}}}$In this expression, N is the number of pixels in the ring region in theimage, x_(i) is the pixel intensity, and {tilde over (x)} is the meanpixel intensity. The kurtosis is particularly useful in distinguishingdiffuse Golgi from normal or dispersed Golgi. Particularly, highervalues of kurtosis suggest a higher degree of the Golgi “diffusion.”

The singular value decomposition is technique that operates on a matrixof pixel intensity values arranged in their relative spatial positionsas in the image. Preferably, these pixel intensities are obtained from aring region as described above. The relevant values are obtained bymultiplying the matrix with its transpose and obtaining the eigenvalues.The singular value decomposition provides information about the textureof the Golgi complex in the image. Thus, it is useful in distinguishingdifferent states of the Golgi complex.

One aspect of the present invention is the ability to estimate theamount of Golgi (or a component of the Golgi) in a given cell or regionof a cell. This allows one to characterize cells based upon relative orabsolute quantity of a particular Golgi component within some region ofthe cell such as the perinuclear region. In general, an agent applied tohighlight Golgi should emit a signal that is proportional to the amountof agent that has bound to a Golgi component. Thus, the amount of signal(usually indicated by the signal intensity summed or integrated over theentire region of interest) provides a direct indication of the quantityof a Golgi component present. When this is the case, the presentinvention allows one to obtain an accurate measure of the amount ofGolgi in a given cell or cell region.

Techniques for estimating the quantity of DNA in a cell nucleus aredescribed U.S. patent application Ser. No. 09/729,754, previouslyincorporated by reference. These techniques may involve subtractingbackground signal and other image processing procedures. In general,such techniques apply to estimating the quantity of Golgi in a cell orregion of a cell.

Classification of Golgi

Optionally, the algorithms of this invention may specifically classifythe Golgi complex into one or more biologically relevant classificationbased upon parameters such as those described above. As mentionedpreviously, one example of biologically relevant classifications includethe four classifications depicted in FIGS. 1A–1D. In the context of thegeneral process flow depicted in FIG. 3, the biologically relevantclassification takes place at block 307. This operation takes as itsinputs morphological, textual, and/or statistical parameters andprovides as an output the biological relevant classifications. Itaccomplishes this using an appropriate model provided in the form of aneural network, linear or non-linear mathematical expression, a tree orgraph, and the like. As previously, mentioned, one preferred approachemploys a classification and regression tree.

Classification and regression trees (CART) are well known tools forclassifying objects. They are described in Brieman et al., (1984)Classification and Regression Trees. Monterey: Wadswirth andBrooks/Cole. FIG. 8 depicts one example of a classification andregression tree (CART) 800. In this example, there are three inputparameters and four possible classifications. The input parameters areidentified as, f₁, f₂, and f₃. The classifications are denoted C₁, C₂,C₃, and C₄. In this hypothetical example, the tree initially considersthe input parameter f₃ If the value of f₃ is less than 0.1, the treebranches in one direction. If, on the other hand, the value of f₃ isgreater than or equal to 0.1, the tree branches in the oppositedirection. Assuming that the value of f₃ is less than 0.1, the tree nextrequires that the parameter f₂ be considered. In this example, if thevalue of f₂ is less than 100, the model classifies the Golgi complex inC₁. On the other hand, if the value of f₂ is greater than or equal to100 the model classifies the Golgi complex in C₂. Similarly, if thevalue of f₃ is found to be greater than or equal to 0.1, then the valueof the input parameter f₁ is considered. Should the value of f₁ be lessthan 3, the tree classifies the Golgi complex in C₃. Finally, if thevalue of f₁ is found to be greater than or equal to 3, then the modelclassifies the Golgi complex in C₄.

Obviously, the parameters f₁, f₂, and f₃ will be relevant toclassification of Golgi, in accordance with this invention. For example,f₁ might correspond to the mean pixel intensity of the Golgi channel, f₂might correspond to the standard deviation of the pixel intensity valuesof the Golgi channel, and f₃ might correspond to the second, third,and/or fourth singular value decomposition of a matrix of pixelintensity values in the ring region. The classification C₁ through C₄might correspond to the Golgi complex states depicted in FIGS. 1Athrough 1D.

Classification models may be generated using numerous techniques. Theseinclude regression analyses (e.g., techniques for generating CARTs),neural networks, maximum likelihood/mixture models, etc. In general, anymodel requires a valid training set containing numerous samples thatspan the range of likely cases that will be encountered in practice. Thesamples should span a wide range of classification types, includingtypes that vary in degree between extreme end cases (e.g., thediffuse/disperse Golgi state). In addition, the samples should include awide range of input parameters. Each sample will have an ascribedclassification and clearly defined input parameters. In this case, itwill typically be necessary for an experienced scientist to classifyimages based upon their Golgi state. These classifications together withthe relevant parameters (e.g. mean, standard deviation, kurtosis, andsingular value decomposition) are provided to a tool for generating theappropriate classification model.

To maximize the predictive accuracy of the model, it may be appropriateto generate separate models for specific ranges of conditions.Typically, a separate model will be generated for each different cellline to be considered, because the appearance of the Golgi complexvaries widely from cell line to cell line. Further, settings associatedwith the image analysis apparatus may cause significant variations inthe image analysis. Therefore, separate models may be appropriate fordifferent image analysis settings such as magnification, illuminationintensity and the like. As noted, the Golgi complex may be imaged usingvarious components contained within the Golgi complex. For example, somecomponents may be detected with labeled lectins and other markers may bedetected with labeled antibodies. Each of these separatecomponent-marker combinations may deserve its own model, assuming morethan one marker is used for the Golgi complex.

As indicated above, certain embodiments of the invention do notnecessarily employ a classification operation. In these embodiments, thealgorithm simply employs the parameters generated a block 305 in orderto draw conclusions about a population of cells. Or alternatively theanalysis tool may generate coefficients for biologically relevantfeatures such as diffusion. In one example, the kurtosis of the Golgiimage (or some derivation from kurtosis) serves as a coefficient fordiffusion. Further, the system could output both the biologicallyrelevant classification of end states and the particular parameters usedto generate these classifications.

Analysis of Populations of Cells

As mentioned, the most useful biological information often comes fromimages of a population of cells. The population may comprise a verylimited range of variations, such as a single cell treated with a singledrug at a single concentration. Or it may comprise a diverse set ofvariations such as multiple cell lines, each treated with multipleconcentrations of a drug (or even multiple drugs believed to operate viaa single mechanism of action).

Note that many images used with the present invention will contain manydiscrete cells. The images often show several hundreds of cells,although the actual number depends on the application. Such cells maycollectively comprise the population of interest. Or multiple wells or asubset of a single well may comprise the population.

Regardless of how the population is defined, it should preferablycontain a relevant sample of cells exposed to a condition of interest.From this population, conclusions about a given stimulus' effect on theGolgi complex can be drawn. The Golgi complex in the population can becharacterized by the percent of cells in each primary category of Golgiend state or by a distribution of Golgi specific parameters across thepopulation or by some other measure of Golgi parameters and/or classesacross the population. As mentioned, interphase cells typically hold themost relevant information about an effect of a stimulus on the Golgicomplex. This is because unperturbed interphase cells typically havenormal Golgi of the type depicted in FIG. 1A. Deviations from thisnormal state, such as illustrated in FIGS. 1B, 1C, and 1D, provide cluesabout the effect of a stimulus. Mitotic cells, in contrast, do notexhibit the “normal” Golgi state even in the absence of an additionalstimulus and instead exhibit the diffuse and/or disperse state.Therefore, it is useful to characterize cells based on their position inthe cell cycle—at least as either mitotic or interphase.

As explained in U.S. patent application Ser. No. 09/729,754, previouslyincorporated by reference, an image of a cells nucleus (DNA) can beanalyzed to distinguish between the G₁, S, and G₂, and M phases of acell. Briefly, the total amount of DNA in a cell nucleus can indicatewhether an interphase cell is in the G₁, S, or G₂ phase. Anotherparameter or grouping of parameters can discriminate between mitotic andinterphase cells. One example of such parameter is the variance inintensity exhibited by an indicator of DNA concentration. One example ofa group of parameters is average pixel intensity and area of thenucleus. In this second example, the average pixel intensity is theaverage of the pixel-based intensities of the nucleus and “area” is thetotal area of the nucleus.

In a particularly preferred embodiment, the cells of a population arefirst characterized using marked DNA. This classification at leastdistinguishes between mitotic and interphase cells. Then, the same cellsare characterized using marked Golgi. Particular attention is paid tothe Golgi complex of the interphase cells.

Software/Hardware

Generally, embodiments of the present invention employ various processesinvolving data stored in or transferred through one or more computersystems. Embodiments of the present invention also relate to anapparatus for performing these operations. This apparatus may bespecially constructed for the required purposes, or it may be ageneral-purpose computer selectively activated or reconfigured by acomputer program and/or data structure stored in the computer. Theprocesses presented herein are not inherently related to any particularcomputer or other apparatus. In particular, various general-purposemachines may be used with programs written in accordance with theteachings herein, or it may be more convenient to construct a morespecialized apparatus to perform the required method steps. A particularstructure for a variety of these machines will appear from thedescription given below.

In addition, embodiments of the present invention relate to computerreadable media or computer program products that include programinstructions and/or data (including data structures) for performingvarious computer-implemented operations. Examples of computer-readablemedia include, but are not limited to, magnetic media such as harddisks, floppy disks, and magnetic tape; optical media such as CD-ROMdisks; magneto-optical media; semiconductor memory devices, and hardwaredevices that are specially configured to store and perform programinstructions, such as read-only memory devices (ROM) and random accessmemory (RAM). The data and program instructions of this invention mayalso be embodied on a carrier wave or other transport medium. Examplesof program instructions include both machine code, such as produced by acompiler, and files containing higher level code that may be executed bythe computer using an interpreter.

FIG. 9 illustrates a typical computer system that, when appropriatelyconfigured or designed, can serve as an image analysis apparatus of thisinvention. The computer system 900 includes any number of processors 902(also referred to as central processing units, or CPUs) that are coupledto storage devices including primary storage 906 (typically a randomaccess memory, or RAM), primary storage 904 (typically a read onlymemory, or ROM). CPU 902 may be of various types includingmicrocontrollers and microprocessors such as programmable devices (e.g.,CPLDs and FPGAs) and unprogrammable devices such as gate array ASICs orgeneral purpose microprocessors. As is well known in the art, primarystorage 904 acts to transfer data and instructions uni-directionally tothe CPU and primary storage 906 is used typically to transfer data andinstructions in a bi-directional manner. Both of these primary storagedevices may include any suitable computer-readable media such as thosedescribed above. A mass storage device 908 is also coupledbi-directionally to CPU 902 and provides additional data storagecapacity and may include any of the computer-readable media describedabove. Mass storage device 908 may be used to store programs, data andthe like and is typically a secondary storage medium such as a harddisk. It will be appreciated that the information retained within themass storage device 908, may, in appropriate cases, be incorporated instandard fashion as part of primary storage 906 as virtual memory. Aspecific mass storage device such as a CD-ROM 914 may also pass datauni-directionally to the CPU.

CPU 902 is also coupled to an interface 910 that connects to one or moreinput/output devices such as such as video monitors, track balls, mice,keyboards, microphones, touch-sensitive displays, transducer cardreaders, magnetic or paper tape readers, tablets, styluses, voice orhandwriting recognizers, or other well-known input devices such as, ofcourse, other computers. Finally, CPU 902 optionally may be coupled toan external device such as a database or a computer ortelecommunications network using an external connection as showngenerally at 912. With such a connection, it is contemplated that theCPU might receive information from the network, or might outputinformation to the network in the course of performing the method stepsdescribed herein.

In one embodiment, the computer system 900 is directly coupled to animage acquisition system such as an optical imaging system that capturesimages of cells. Digital images from the image generating system areprovided via interface 912 for image analysis by system 900.Alternatively, the images processed by system 900 are provided from animage storage source such as a database or other repository of cellimages. Again, the images are provided via interface 912. Once in theimage analysis apparatus 900, a memory device such as primary storage906 or mass storage 908 buffers or stores, at least temporarily, digitalimages of the cell. Typically, the cell images will show locations whereGolgi, and possibly DNA, exists within the cells. In these images, localvalues of a Golgi image parameter (e.g., radiation intensity) correspondto amounts of a Golgi component at the locations within the cell shownon the image. With this data, the image analysis apparatus 900 canperform various image analysis operations such as distinguishing betweennormal and diffuse and/or dispersed Golgi and estimating the amount ofGolgi in a cell. To this end, the processor may perform variousoperations on the stored digital image. For example, it may analyze theimage in manner that extracts values of one or more Golgi stateparameters that correspond to a Golgi primary state and classifies thecell as either normal of diffuse, disperse or diffuse/disperse basedupon the extracted values of the parameters. Alternatively, or inaddition, it may estimate a total value of the Golgi image parametertaken over at least a perinuclear region of the cell.

EXAMPLES

The following examples each involved generation and analysis of imagesobtained from SKOV3 cells treated with a Lens culinaris lectin marker.The cells were incubated with each drug for 24 hours before fixation andstaining. In each case, ring regions were obtained by the Golgisegmentation algorithm described above. In addition, the Golgi complexwithin those ring regions was predicted, using a CART model as describedabove, as either normal, diffuse, dispersed, or diffuse and dispersed.

In a first example, SKOV3 cells that were treated with a lowconcentration (0.6 nanomolar) of the anti-microtubule drug Taxol. Inthis example, the Golgi complex remains normal as determined by themodel. An expert independently examined the images and concluded thatthe Golgi complex was normal. In a second example, SKOV3 cells treatedwith a high concentration (2.6 micromolar) of microtubule-depolymerizing drug Nocodazole. Using the same model, the Golgi complexwas found to be dispersed. And the expert independently confirmed thatthe Golgi complex was in fact dispersed. Finally, in a third example,SKOV3 cells were treated with a drug that disrupts transport into andout of the Golgi complex—Brefeldin A. The drug was provided in aconcentration of 79 micromolar. In this case, the image analysisalgorithm classified the Golgi complex as diffuse. The expertindependently confirmed this.

Conclusion

Although the above has generally described the present inventionaccording to specific processes and apparatus, the present invention hasa much broader range of applicability. In particular, the presentinvention is not limited to a particular kind of cell component, notjust the Golgi complex. Thus, in some embodiments, the techniques of thepresent invention could provide information about many different typesor groups of cellular organelles and components of all kinds. Of course,one of ordinary skill in the art would recognize other variations,modifications, and alternatives.

1. A method, implemented on a computing device, of analyzing an image ofone or_more cells, the method comprising: (a) identifying a region ofthe image subsuming some or all of the Golgi complex of a single cell;(b) within said region, automatically identifying the location of theGolgi complex; (c) based upon at least one of (i) the Golgi complexlocation within the region and (ii) the Golgi concentration within theregion, automatically mathematically characterizing the Golgi complexwithin the single cell by calculating one or more of the kurtosis ofintensity values associated with Golgi complex, an eigenvalue of asingular value decomposition of intensity values associated with Golgicomplex, a mean of intensity values associated with Golgi complex, and astandard deviation of intensity values associated with Golgi complex;and (d) applying the mathematical characterization to a Golgiclassification model to thereby automatically classify the Golgi complexin a category that at least partially distinguishes between normal Golgiand Golgi that is either diffuse or disperse or both diffuse anddisperse.
 2. The method of claim 1, wherein the image comprises multiplecells, and wherein identifying the region of the image subsuming some orall of the Golgi complex comprises segmenting the image into regions,each subsuming at least part of an individual cell.
 3. The method ofclaim 2, wherein segmenting comprises identifying locations of nuclei inthe cells.
 4. The method of claim 3, wherein the nuclei are identifiedby identifying regions of the image where DNA is shown to concentrate.5. The method of claim 3, further comprising within the image dilatingthe locations of the nuclei to subsume the locations of some or all ofthe Golgi complex in the individual cells.
 6. The method of claim 1,wherein the image depicts concentration versus position of one or moreGolgi components within the cell.
 7. The method of claim 6, wherein theconcentration of at least one Golgi component corresponds to intensityin the image.
 8. The method of claim 1, wherein one or more cells in theimage were treated with a material that binds to a component of theGolgi complex and emits a signal having an intensity corresponding toits concentration.
 9. The method of claim 8 wherein the material is alectin or antibody that binds to the component of the Golgi complex. 10.The method of claim 1, wherein the mathematical characterization of theGolgi complex comprises at least one of (i) an indicator of thepeakedness of a histogram of at least one component of the Golgicomplex, (ii) the texture of the Golgi complex and (iii) and the amountof Golgi complex in the region.
 11. The method of claim 1, wherein themathematical characterization of the Golgi complex comprises at leastone of a mean and a standard deviation of intensity values obtained fromthe image.
 12. The method of any one of claims 1 or 11, wherein theintensity values correspond to local concentrations of a component ofthe Golgi complex within the single cell.
 13. The method of claim 1,wherein classifying the Golgi complex comprises analyzing themathematical characterization with a biological model of the Golgicomplex.
 14. The method of claim 13, wherein the biological model is aregression model or a neural network.
 15. The method of claim 13,wherein the biological model is a classification and regression tree.16. The method of claim 1, further comprising characterizing apopulation of cells from the image by considering the category of Golgicomplex in each cell of the population.
 17. The method of claim 16,further comprising predicting a mechanism of action from thecharacterization of the population.
 18. A computer program productstored on a computer readable medium and comprising program instructionsfor analyzing an image of one or more cells, the program instructionscomprising: (a) program code for identifying a region of the imagesubsuming some or all of the Golgi complex of a single cell; (b) programcode for automatically identifying, within said region, the location ofthe Golgi complex; (c) program code for using at least one of (i) theGolgi complex location within the region and (ii) the Golgiconcentration within the region, to automatically mathematicallycharacterize the Golgi complex within the single cell by calculating oneor more of the kurtosis of intensity values obtained from the image, aneigenvalue of a singular value decomposition of intensity valuesassociated with Golgi complex, (c) a mean of intensity values associatedwith Golgi complex, (d) a standard deviation of intensity valuesassociated with Golgi complex; and (d) program code for applying themathematical characterization to a Golgi classification model to therebyautomatically classify the Golgi complex in a category that at leastpartially distinguishes between normal Golgi and Golgi that is eitherdiffuse or disperse or both diffuse and disperse.
 19. The computerprogram product of claim 18, wherein the image comprises multiple cells,and wherein the program code for identifying the region of the imagesubsuming some or all of the Golgi complex comprises program code forsegmenting the image into regions, each subsuming at least part of anindividual cell.
 20. The computer program product of claim 19, whereinthe program code for segmenting comprises program code for identifyinglocations of nuclei in the cells.
 21. The computer program product ofclaim 20, wherein the program code for identifying locations of nucleicomprises program code for identifying regions of the image where DNA isshown to concentrate.
 22. The computer program product of claim 20,further comprising program code for dilating the locations of the nucleiwithin the image to subsume the locations of some or all of the Golgicomplex in the individual cells.
 23. The computer program product ofclaim 18, wherein the image depicts concentration versus position of oneor more Golgi components within the cell.
 24. The computer programproduct of claim 18, wherein the mathematical characterization of theGolgi complex comprises at least one of (i) an indicator of thepeakedness of a histogram of at least one component of the Golgicomplex, (ii) the texture of the Golgi complex and (iii) and the amountof Golgi complex in the region.
 25. The computer program product ofclaim 18, wherein the program code for classifying the Golgi complexcomprises program code for analyzing the mathematical characterizationwith a biological model of the Golgi complex.
 26. The computer programproduct of claim 25, wherein the biological model is a regression modelor a neural network.
 27. The computer program product of claim 25,wherein the biological model is a classification and regression tree.28. The computer program product of claim 18, further comprising programcode for characterizing a population of cells from the image byconsidering the category of Golgi complex in each cell of thepopulation.
 29. The computer program product of claim 28, furthercomprising program code for predicting a mechanism of action from thecharacterization of the population.
 30. An apparatus for automaticallyanalyzing an image of one or more cells, the apparatus comprising: aninterface configured to receive the image of one or more cells; a memoryfor storing, at least temporarily, some or all of the image; and one ormore processors in communication with the memory and designed orconfigured to segment the image into discrete regions, each subsumingsome or all of the Golgi complex in single cell; mathematicallycharacterize the Golgi complex of single cells by calculating one ormore of (a) a kurtosis of intensity values obtained from the image, (b)an eigenvalue of a singular value decomposition of intensity valuesassociated with Golgi complex, (c) a mean of intensity values associatedwith Golgi complex, (d) a standard deviation of intensity valuesassociated with Golgi complex; and apply the mathematicalcharacterization to a Golgi classification model to therebyautomatically classify the Golgi complex in a category that at leastpartially distinguishes between normal Golgi and Golgi that is diffuseor disperse.
 31. The apparatus of claim 30, wherein the one or moreprocessors classifies the Golgi complex by using a biological model. 32.The apparatus of claim 31, wherein the biological model is aclassification and regression tree.
 33. The apparatus of claim 30,wherein the one or more processors segment the image by firstidentifying regions of the image corresponding to nuclei of the one ormore cells.
 34. The apparatus of claim 33, wherein the one or moreprocessors perform a dilation operation at the regions of the nuclei onthe image in order to subsume the Golgi complex of each of the one ormore cells.
 35. The apparatus of claim 30, wherein the image to bereceived by the interface depicts concentration versus position of oneor more Golgi components within the cell.
 36. The apparatus of claim 35,wherein the one or more Golgi components correspond to intensity valuesin the image to be received by the interface.
 37. The apparatus of claim30, wherein the one or more cells in the image were treated with amaterial that binds to a component of the Golgi complex and emits asignal having an intensity corresponding to its concentration.
 38. Theapparatus of claim 30, wherein the one or more processors mathematicallycharacterize the Golgi complex by calculating at least one or (i) anindicator of the peakedness of a histogram of at least one component ofthe Golgi complex, (ii) the texture of the Golgi complex, and (iii) theamount of Golgi complex in the discrete regions.
 39. The apparatus ofclaim 30, wherein the one or more processors is further designed orconfigured to characterize a population comprising the one or more cellsby considering a category of Golgi for each of the one or more cells.40. The apparatus of claim 39, wherein the one or more processors isfurther designed or configured to predict a mechanism of action fromcharacterizing the population.